Vector Space Models (VSMs) represent documents as points in a vector space derived from term frequencies in the corpus. This level of abstraction provides a flexible way to represent complex semantic concepts through vectors, matrices, and higher-order tensors. In this paper we utilize a number of VSMs on a corpus of judicial decisions in order to classify cases in terms of legal factors, stereotypical fact patterns that tend to strengthen or weaken a side's argument in a legal claim. We apply different VSMs to a corpus of trade secret misappropriation cases and compare their classification results. The experiment shows that simple binary VSMs work better than previously reported techniques but that more complex VSMs including dimensionality reduction techniques do not improve performance.